The proposed method is based on the Faster R-CNN baseline [1]}, pre-trained on the ImageNet. We optimize the network using the Stochastic Gradient Descent (SGD) algorithm with 0.9 momentum and 0.0005 weight decay, which is trained on 1 1080Ti GPU with the mini-batch involving 1 image per GPU. For Caltech dataset, we train the network for \(40k\)  iterations with the initial learning rate of \(10^{-3}\)  and decay it to \(10^{-4}\)  for another \(20k\)  iterations. For Citypersons dataset, we train the network for \(20k\)  iterations with the initial learning rate of \(10^{-3}\)  and decay it to \(10^{-4}\)  for another \(10k\)  iterations. All images are in the original \(1\mathrm {x}\) . scale during training and testing. Other parameters \(\alpha \) , \(\beta \)  and \(\gamma \)  are set to 1, 1 and 0.01, respectively. \(N_p\)  and \(N_e\)  are both equal to 256 as usual. Specifically, our depth estimation model is VNL [2]}. Better depth estimation model may have better performance, but it is not in the main scope of ours method.
